Designing Big Data Platforms


How to Use, Deploy, and Maintain Big Data Systems

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ISBN: 9781119690924 Categories: ,

<b>DESIGNING BIG DATA PLATFORMS</b> <p><b>Provides expert guidance and valuable insights on getting the most out of Big Data systems</b><p>An array of tools are currently available for managing and processing data—some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. <i>Designing Big Data Platforms</i> provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems.<p>This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies:<ul><li>Provides up-to-date coverage of the tools currently used in Big Data processing and management</li><li>Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems</li><li>Highlights and explains how data is processed at scale</li><li>Includes an introduction to the foundation of a modern data platform</li></ul><p><i>Designing Big Data Platforms: How to Use, Deploy, and Maintain Big Data Systems</i> is a must-have for all professionals working with Big Data, as well researchers and students in computer science and related fields.


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